Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality
A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.
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People can accept tradeoffs when they see value — but if they don’t, what happens?
The post Understanding Anti-AI Public Opinion appeared first on Towards Data Science.
Webwright: Why AI Web Agents Should Write Code, Not Click
For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And inste...
Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them
How autonomous agents broke two decades of capacity planning — and what to build instead
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How to Perform Effective Project Management with AI
Become a more productive software engineer with LLMs
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Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
A small experiment in remote SQL execution
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How to Shine as a Data Scientist in the Vibe Coding Era
Here's how to be the Data Scientist who thrives in a world where coding is a commodity.
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Becoming Human AI Is Expanding — Here’s What’s Changing
For years, this has been where you’ve found us — through Medium, whenever we published something worth your time.That’s changing a little, starting now.We’re expanding into a proper newsletter, so expect to hear from us more regularly than “whenever a new post goes up.” If you’ve got email notificat...
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) — and shows the 2...
LangChain vs LangGraph: 4 Key Differences and When to Use Each
A practical guide to choose the proper tool for your agentic workflows and systems
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In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance generative responses from a large language model for specific applications in that particular domain.It allows the large language model...
NLP in 2026: Trends, Use Cases & Future of Language AI | Shaip
Every day, your organization produces a mountain of words. Support tickets, contracts, clinical notes, customer reviews, emails, call transcripts. Roughly 80% of all enterprise data exists as unstructured text like this — and until recently, almost none of it could be analyzed at scale. It just sat ...
Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works
From one gradient to every gradient
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How to Place Vertiport Locations in Any City Using Geospatial Machine Learning
A reproducible Lagos case study with population data, transport access, and airspace constraints
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Should AI Developers Make the Switch from Polars to Pandas?
Not all Python data libraries are created equal!
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How to diversify a budget without losing the shadow prices that explain the result
The post The Budget Split That Explains Itself appeared first on Towards Data Science.
Learn how to optimize your CI/CD pipeline for coding agents
The post How to Effectively Deploy Code With Claude Code appeared first on Towards Data Science.
Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick
A clear, math-first walkthrough of how VAEs learn to generate new data
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SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint
Learn how Spatial Pyramid Pooling enables CNNs to handle any image size, with a from-scratch PyTorch implementation
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How to Implement Structured Output with Local LLMs
Why use it? How to implement it? What can we do when it fails?
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